np.clip is a function in the NumPy library that is used to limit the values in an array to a specified range. This function takes three arguments: the array that you want to clip, a minimum value, and a maximum value. It then returns a new array with the values clipped to fall within the specified range.

One common use case for np.clip is to ensure that values in an array do not exceed certain thresholds. For example, if you have an array of temperatures and you want to ensure that all temperatures are within the range of 0 to 100 degrees, you can use np.clip to limit the values to that range.

Here's a simple example of how to use np.clip:

```python import numpy as np

# Create an array of temperatures temperatures = np.array([-10, 20, 50, 110])

# Clip the values to fall within the range of 0 to 100 clipped_temperatures = np.clip(temperatures, 0, 100)

print(clipped_temperatures) ```

In this example, the original array of temperatures is [-10, 20, 50, 110]. After using np.clip to limit the values to the range of 0 to 100, the resulting array is [0, 20, 50, 100].

np.clip can also be used with multi-dimensional arrays. When working with multi-dimensional arrays, you can specify different minimum and maximum values for each axis of the array. This allows you to clip values along specific dimensions.

Here's an example of clipping a multi-dimensional array:

```python import numpy as np

# Create a 2D array of random values array = np.random.random((3, 3))

# Clip the values along the rows to fall within the range of 0.2 to 0.8 clipped_array = np.clip(array, 0.2, 0.8, axis=1)

print(clipped_array) ```

In this example, the original 2D array has random values ranging from 0 to 1. By using np.clip with the axis=1 argument, the values along each row are clipped to fall within the range of 0.2 to 0.8.

Overall, np.clip is a useful function in NumPy for limiting the values in an array to a specified range. It is commonly used in data processing and analysis tasks to enforce constraints on the values of arrays. By understanding how np.clip works and how to use it effectively in your code, you can ensure that your data remains within the desired boundaries.